SoroushVahidi/scidocs huggingface dataset:
The core role of the nurse practitioner: practice, professionalism and clinical leadership.
(2007)

AIM
To draw on empirical evidence to illustrate the core role of nurse practitioners in Australia and New Zealand.


BACKGROUND
Enacted legislation provides for mutual recognition of qualifications, including nursing, between New Zealand and Australia. As the nurse practitioner role is relatively new in both countries, there is no consistency in role expectation and hence mutual recognition has not yet been applied to nurse practitioners. A study jointly commissioned by both countries' Regulatory Boards developed information on the core role of the nurse practitioner, to develop shared competency and educational standards. Reporting on this study's process and outcomes provides insights that are relevant both locally and internationally.


METHOD
This interpretive study used multiple data sources, including published and grey literature, policy documents, nurse practitioner program curricula and interviews with 15 nurse practitioners from the two countries. Data were analysed according to the appropriate standard for each data type and included both deductive and inductive methods. The data were aggregated thematically according to patterns within and across the interview and material data.


FINDINGS
The core role of the nurse practitioner was identified as having three components: dynamic practice, professional efficacy and clinical leadership. Nurse practitioner practice is dynamic and involves the application of high level clinical knowledge and skills in a wide range of contexts. The nurse practitioner demonstrates professional efficacy, enhanced by an extended range of autonomy that includes legislated privileges. The nurse practitioner is a clinical leader with a readiness and an obligation to advocate for their client base and their profession at the systems level of health care.


CONCLUSION
A clearly articulated and research informed description of the core role of the nurse practitioner provides the basis for development of educational and practice competency standards. These research findings provide new perspectives to inform the international debate about this extended level of nursing practice.


RELEVANCE TO CLINICAL PRACTICE
The findings from this research have the potential to achieve a standardised approach and internationally consistent nomenclature for the nurse practitioner role.
Event-based Camera Pose Tracking using a Generative Event Model
(2015)

Event-based vision sensors mimic the operation of biological retina and they represent a major paradigm shift from traditional cameras. Instead of providing frames of intensity measurements synchronously, at artificially chosen rates, event-based cameras provide information on brightness changes asynchronously, when they occur. Such non-redundant pieces of information are called “events”. These sensors overcome some of the limitations of traditional cameras (response time, bandwidth and dynamic range) but require new methods to deal with the data they output. We tackle the problem of event-based camera localization in a known environment, without additional sensing, using a probabilistic generative event model in a Bayesian filtering framework. Our main contribution is the design of the likelihood function used in the filter to process the observed events. Based on the physical characteristics of the sensor and on empirical evidence of the Gaussian-like distribution of spiked events with respect to the brightness change, we propose to use the contrast residual as a measure of how well the estimated pose of the event-based camera and the environment explain the observed events. The filter allows for localization in the general case of six degrees-of-freedom motions.
Zero quiescent current, delay adjustable, power-on-reset circuit
(2014)

A power-on-reset (POR) circuit is extremely important in digital and mixed-signal ICs. It is used to initialize critical nodes of digital circuitry inside the chip during power-on. A novel POR circuit having zero quiescent current and an adjustable delay is proposed and demonstrated. The circuit has been designed in 0.5μm CMOS process to work for a supply voltage ranging from 1.8V to 5.5V. The circuit generates a POR signal after a predetermined delay, after the supply voltage crosses a predefined threshold voltage. This delay can be increased or decreased via programmable fuses. The POR threshold does not depend upon the supply ramp rate. The Brown-Out (BO) voltage for the proposed circuit matches the minimum supply voltage required by the digital circuitry inside the IC. The circuit consumes zero steady state current, making it ideal for low power applications.
Mobile application for guiding tourist activities: tourist assistant - TAIS
(2014)

The paper presents category classification of mobile travel applications accessible at the moment for tourists in application stores for most popular mobile operation systems (Android and iOS). The most interesting category is "Travel Guides" that combines "Information Resources" and "Location-Based Services" category. Authors propose application "Tourist assistant - TAIS" that is related to "Travel Guides" category and recommends the tourist attractions around. Information about attractions is extracted from different internet sources.
A novel isolated electrolytic capacitor-less single-switch AC-DC offline LED driver with power factor correction
(2014)

Conventional AC-DC driver circuits for Light-Emitting Diode (LED) lamps require large output capacitance across the LED load to minimize the low frequency current ripple. This large capacitance is usually achieved by using an electrolytic capacitor, which has a lifetime that is at least two times less than that of a LED device. To match the potential lifetime of the LEDs, a new isolated single switch AC-DC high power factor LED driver without any electrolytic capacitors is proposed in this paper. In the proposed circuit, the energy storage capacitor is moved to the rectifier side, with a three-winding transformer used to provide isolation; power factor correction as well as to store and provide the required energy to the output. As a result, the energy storage capacitance is significantly reduced, which allows film capacitor to replace the unreliable electrolytic capacitors. The circuit's operating principles and its characteristics are described in this paper. Simulation and experimental results confirm that a power factor of 0.96 is achieved on a 120Vrms, 12W prototype.
Lightweight Cryptography for FPGAs
(2009)

The advent of new low-power Field Programmable Gate Arrays (FPGA) for battery powered devices opens a host of new applications to FPGAs. In order to provide security on resource constrained devices lightweight cryptographic algorithms have been developed. However, there has not been much research on porting these algorithms to FPGAs. In this paper we propose lightweight cryptography for FPGAs by introducing block cipher independent optimization techniques for Xilinx Spartan3 FPGAs and applying them to the lightweight cryptographic algorithms HIGHT and Present. Our implementations are the first reported of these block ciphers on FPGAs. Furthermore, they are the smallest block cipher implementations on FPGAs using only 117 and 91 slices respectively, which makes them comparable in size to stream cipher implementations. Both are less than half the size of the AES implementation by Chodowiec and Gaj without using block RAMs. Present’s throughput over area ratio of 240 Kbps/slice is similar to that of AES, however, HIGHT outperforms them by far with 720 Kbps/slice.
Pixel Recurrent Neural Networks
(2016)

Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast twodimensional recurrent layers and an effective use of residual connections in deep recurrent networks. We achieve log-likelihood scores on natural images that are considerably better than the previous state of the art. Our main results also provide benchmarks on the diverse ImageNet dataset. Samples generated from the model appear crisp, varied and globally coherent.
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields
(2017)

We present an approach to efficiently detect the 2D pose of multiple people in an image. The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. The architecture encodes global context, allowing a greedy bottom-up parsing step that maintains high accuracy while achieving realtime performance, irrespective of the number of people in the image. The architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process. Our method placed first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.
Conditional Generative Adversarial Nets
(2014)

Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels.
Efficiency of low power audio amplifiers and loudspeakers
(2001)

In this paper we look at the load presented to audio amplifiers by real transducers. We consider the power losses in Class-AB and Class-D amplifier topologies, and determine that in order to predict efficiency it is necessary to consider the amplifier/transducer combination. The ability of the class-D amplifier to recycle quadrature load current offers new ways to improve efficiency. INTRODUCTION Class-D amplifiers are beginning to become viable alternatives to the Class-AB amplifier because of the reducing cost of suitable devices. There is particular interest in developing them for low power applications where their intrinsic efficiency advantages are important. The operation of Class-D amplifiers is very different to Class-AB amplifiers and this has implications for other components in the audio chain. Conventionally the performance of audio amplifiers is considered using a pure resistive load of four or eight ohms. The origin of this lies in the nominal impedance given to electro-magnetic loudspeakers. The true impedance of an electro-magnetic loudspeaker will vary over the operating frequency range, but the variability between individual real transducers makes the definition of a more realistic ‘bench-mark’ load impractical. Other audio transducers also have a complex impedance. Transducers which utilise piezo electric elements have a input impedance that is highly reactive. The load presented to the amplifier is almost entirely capacitive. The power output and efficiency of an amplifier are dependent load impedance and so the resistive load performance may not resemble to the operation of the amplifier in real situations. In this paper we will look at the true load presented to amplifiers by the transducer and derive a more accurate measure of overall efficiency. GENERAL AMPLIFIER EFFICIENCY The electrical efficiency of an amplifier is defined as the ratio of the power developed in the load to the power drawn from the DC supply. Using simple linear analysis we can determine the efficiency of amplifier output stages and the dependence of the efficiency on load parameters. In this section the following symbols are used; Vo Output voltage Vs Supply rail voltage RL Load resistance Ibias Class AB quiescent bias current φ Load phase angle Zload Load impedance L Class D filter inductance Rin Resistance of filter inductor RDson ‘On’ state resistance of switching devices fs Class D Switching frequency Class-AB resistive case A simple Class-AB output stage is shown in figure 1. A complementary pair of output devices operate over their linear region to amplify the signal. When the devices are operated in the linear region there will be current flowing through them whilst there is a voltage across them, this will give rise to power dissipation, and hence reduce efficiency. The devices also need a quiescent bias to reduce crossover distortion as one device takes over from the other. Load
Impact of a contemplative end-of-life training program: being with dying.
(2009)

OBJECTIVE
Health care professionals report a lack of skills in the psychosocial and spiritual aspects of caring for dying people and high levels of moral distress, grief, and burnout. To address these concerns, the "Being with Dying: Professional Training Program in Contemplative End-of-Life Care" (BWD) was created. The premise of BWD, which is based on the development of mindfulness and receptive attention through contemplative practice, is that cultivating stability of mind and emotions enables clinicians to respond to others and themselves with compassion. This article describes the impact of BWD on the participants.


METHODS
Ninety-five BWD participants completed an anonymous online survey; 40 completed a confidential open-ended telephone interview.


RESULTS
Four main themes-the power of presence, cultivating balanced compassion, recognizing grief, and the importance of self-care-emerged in the interviews and were supported in the survey data. The interviewees considered BWD's contemplative and reflective practices meaningful, useful, and valuable and reported that BWD provided skills, attitudes, behaviors, and tools to change how they worked with the dying and bereaved.


SIGNIFICANCE OF RESULTS
The quality of presence has the potential to transform the care of dying people and the caregivers themselves. Cultivating this quality within themselves and others allows clinicians to explore alternatives to exclusively intellectual, procedural, and task-oriented approaches when caring for dying people. BWD provides a rare opportunity to engage in practices and methods that cultivate the stability of mind and emotions that may facilitate compassionate care of dying patients, families, and caregivers.
The influence of geometrical deviations of electrical machine systems on the signal quality of the variable reluctance resolver
(2016)

Variable reluctance resolvers are needed to regulate electrical machines with inverter control. Due to several issues, e.g. discrete windings in the stator and geometric tolerances in the assembly of the electrical machine, the rotors angular position cannot be acquired properly. The subject of this paper is a variable reluctance resolver model to investigate manufacturing induced geometrical tolerances of the electrical machine in means of rotor position. For this purpose, a block diagram of a stator segment is built, connecting 24 segments to one stator core using PLECS. The rotor can be set freely in shape and position. By varying a permeance block the rotation, i.e. the position dependent airgap, is mirrored. The proposed model allows a sensitivity analysis of the sensor signal according to geometrical changes and the absolute position in the electrical machine system. To evaluate the investigations, the results are verified by measurements on a sensor test bench. The simulation results show a similar behavior as the measurement. Although the values differ by a factor of five, this absolute difference is discussed by means of neglected boundary conditions.
Clinical practice guidelines for diagnosis of autism spectrum disorder in adults and children in the UK: a narrative review
(2018)

BACKGROUND
Research suggests that diagnostic procedures for Autism Spectrum Disorder are not consistent across practice and that diagnostic rates can be affected by contextual and social drivers. The purpose of this review was to consider how the content of clinical practice guidelines shapes diagnoses of Autism Spectrum Disorder in the UK; and investigate where, within those guidelines, social factors and influences are considered.


METHODS
We electronically searched multiple databases (NICE Evidence Base; TRIP; Social Policy and Practice; US National Guidelines Clearinghouse; HMIC; The Cochrane Library; Embase; Global health; Ovid; PsychARTICLES; PsychINFO) and relevant web sources (government, professional and regional NHS websites) for clinical practice guidelines. We extracted details of key diagnostic elements such as assessment process and diagnostic tools. A qualitative narrative analysis was conducted to identify social factors and influences.


RESULTS
Twenty-one documents were found and analysed. Guidelines varied in recommendations for use of diagnostic tools and assessment procedures. Although multidisciplinary assessment was identified as the 'ideal' assessment, some guidelines suggested in practice one experienced healthcare professional was sufficient. Social factors in operational, interactional and contextual areas added complexity to guidelines but there were few concrete recommendations as to how these factors should be operationalized for best diagnostic outcomes.


CONCLUSION
Although individual guidelines appeared to present a coherent and systematic assessment process, they varied enough in their recommendations to make the choices available to healthcare professionals particularly complex and confusing. We recommend a more explicit acknowledgement of social factors in clinical practice guidelines with advice about how they should be managed and operationalised to enable more consistency of practice and transparency for those coming for diagnosis.
Navigation of an omni-directional mobile robot with active caster wheels
(2008)

This work deals with navigation of an omni-directional mobile robot with active caster wheels. Initially, the posture of the omni-directional mobile robot is calculated by using the odometry information. Next, the position accuracy of the mobile robot is measured through comparison of the odometry information and the external sensor measurement. Finally, for successful navigation of the mobile robot, a motion planning algorithm that employs kinematic redundancy resolution method is proposed. Through experiments for multiple obstacles and multiple moving obstacles, the feasibility of the proposed navigation algorithm was verified.
Examining a hate speech corpus for hate speech detection and popularity prediction
(2018)

As research on hate speech becomes more and more relevant every day, most of it is still focused on hate speech detection. By attempting to replicate a hate speech detection experiment performed on an existing Twitter corpus annotated for hate speech, we highlight some issues that arise from doing research in the field of hate speech, which is essentially still in its infancy. We take a critical look at the training corpus in order to understand its biases, while also using it to venture beyond hate speech detection and investigate whether it can be used to shed light on other facets of research, such as popularity of hate tweets.
A Comparison of 10 Sampling Algorithms for Configurable Systems
(2016)

Almost every software system provides configuration options to tailor the system to the target platform and application scenario. Often, this configurability renders the analysis of every individual system configuration infeasible. To address this problem, researchers have proposed a diverse set of sampling algorithms. We present a comparative study of 10 state-of-the-art sampling algorithms regarding their fault-detection capability and size of sample sets. The former is important to improve software quality and the latter to reduce the time of analysis. In a nutshell, we found that sampling algorithms with larger sample sets are able to detect higher numbers of faults, but simple algorithms with small sample sets, such as most-enabled-disabled, are the most efficient in most contexts. Furthermore, we observed that the limiting assumptions made in previous work influence the number of detected faults, the size of sample sets, and the ranking of algorithms. Finally, we have identified a number of technical challenges when trying to avoid the limiting assumptions, which questions the practicality of certain sampling algorithms.
Gaussian Process Regression Networks
(2012)

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the nonparametric flexibility of Gaussian processes. GPRN accommodates input (predictor) dependent signal and noise correlations between multiple output (response) variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both elliptical slice sampling and variational Bayes inference procedures for GPRN. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on real datasets, including a 1000 dimensional gene expression dataset.
Microplastics in the marine environment.
(2011)

This review discusses the mechanisms of generation and potential impacts of microplastics in the ocean environment. Weathering degradation of plastics on the beaches results in their surface embrittlement and microcracking, yielding microparticles that are carried into water by wind or wave action. Unlike inorganic fines present in sea water, microplastics concentrate persistent organic pollutants (POPs) by partition. The relevant distribution coefficients for common POPs are several orders of magnitude in favour of the plastic medium. Consequently, the microparticles laden with high levels of POPs can be ingested by marine biota. Bioavailability and the efficiency of transfer of the ingested POPs across trophic levels are not known and the potential damage posed by these to the marine ecosystem has yet to be quantified and modelled. Given the increasing levels of plastic pollution of the oceans it is important to better understand the impact of microplastics in the ocean food web.
Machine Learning and Social Robotics for Detecting Early Signs of Dementia
(2017)

This paper presents the EACare project, an ambitious multidisciplinary collaboration with the aim to develop an embodied system, capable of carrying out neuropsychological tests to detect early signs of dementia, e.g., due to Alzheimer’s disease. The system will use methods from Machine Learning and Social Robotics, and be trained with examples of recorded clinician-patient interactions. The interaction will be developed using a participatory design approach. We describe the scope and method of the project, and report on a first Wizard of Oz prototype.